A new probabilistic transformation of belief mass assignment

In this paper, we propose in Dezert-Smarandache Theory (DSmT) framework, a new probabilistic transformation, called DSmP, in order to build a subjective probability measure from any basic belief assignment defined on any model of the frame of discernment. Several examples are given to show how the DSmP transformation works and we compare it to main existing transformations proposed in the literature so far. We show the advantages of DSmP over classical transformations in term of probabilistic information content (PIC). The direct extension of this transformation for dealing with qualitative belief assignments is also presented.

• Publication year

  2008

• Venue

  Fusion

• Publication date

  2008-07-23

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.2139/SSRN.2725868arXiv 0807.3669
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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